Search Results for author: Tobias Schnabel

Found 18 papers, 4 papers with code

When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?

no code implementations2 May 2023 Yushun Dong, Jundong Li, Tobias Schnabel

In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation.

Memorization Recommendation Systems

Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates

no code implementations11 Nov 2022 Tobias Schnabel, Mengting Wan, Longqi Yang

With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry.

Recommendation Systems Transductive Learning

Where Do We Go From Here? Guidelines For Offline Recommender Evaluation

no code implementations2 Nov 2022 Tobias Schnabel

We present a TrainRec, a lightweight and flexible toolkit for offline training and evaluation of recommender systems that implements these guidelines.

Hyperparameter Optimization Recommendation Systems +1

EvalRS: a Rounded Evaluation of Recommender Systems

1 code implementation12 Jul 2022 Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.

Recommendation Systems

Lightweight Compositional Embeddings for Incremental Streaming Recommendation

no code implementations4 Feb 2022 Mengyue Hang, Tobias Schnabel, Longqi Yang, Jennifer Neville

Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i. e., users and items) is available upfront at training time.

Recommendation Systems

SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

2 code implementations18 Nov 2021 Philippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. Hearst

In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level).

Natural Language Inference Sentence

Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text

1 code implementation ACL 2021 Philippe Laban, Tobias Schnabel, Paul Bennett, Marti A. Hearst

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity.

Reading Comprehension Text Simplification

Deep Generalized Method of Moments for Instrumental Variable Analysis

2 code implementations NeurIPS 2019 Andrew Bennett, Nathan Kallus, Tobias Schnabel

Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible.

Model Selection

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

no code implementations17 Mar 2017 Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims

In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies.


Unbiased Comparative Evaluation of Ranking Functions

no code implementations25 Apr 2016 Tobias Schnabel, Adith Swaminathan, Peter Frazier, Thorsten Joachims

Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge.

Online Updating of Word Representations for Part-of-Speech Tagging

no code implementations EMNLP 2015 Wenpeng Yin, Tobias Schnabel, Hinrich Schütze

We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible.

Online unsupervised domain adaptation Part-Of-Speech Tagging +2

Recommendations as Treatments: Debiasing Learning and Evaluation

no code implementations17 Feb 2016 Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself.

Causal Inference Recommendation Systems

Towards a Better Understanding of Predict and Count Models

no code implementations6 Nov 2015 S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna

In a recent paper, Levy and Goldberg pointed out an interesting connection between prediction-based word embedding models and count models based on pointwise mutual information.

L2 Regularization

Using Shortlists to Support Decision Making and Improve Recommender System Performance

no code implementations26 Oct 2015 Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims

From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality.

Decision Making Movie Recommendation +1

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